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Record W4387955448 · doi:10.1016/j.shaw.2023.10.014

Systematizing Information Use to Address Determinants of Health Worker Health in South Africa: A Cross-sectional Mixed Method Study

2023· article· en· W4387955448 on OpenAlexafffund
Muzimkhulu Zungu, Annalee Yassi, Jonathan Ramodike, Kuku Voyi, Karen Lockhart, David Jones, Spo Kgalamono, Nkululeko Thunzi, Jerry Spiegel

Bibliographic record

VenueSafety and Health at Work · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of British Columbia
FundersNational Health Laboratory ServiceUniversity of PretoriaInternational Development Research Centre
KeywordsCross-sectional studyEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Background: Recognizing that access to safe and healthy working conditions is a human right, the World Health Organization (WHO) calls for specific occupational safety and health (OSH) programs for health workers (HWs). The WHO health systems' building blocks, and the International Labour Organization (ILO), highlight the importance of information as part of effective systems. This study examined how OSH stakeholders access, use, and value an occupational health information system (OHIS). Methods: A cross-sectional survey of OSH stakeholders was conducted as part of a larger quasi experimental study in four teaching hospitals. The study hospitals and participants were purposefully selected and data collected using a modified questionnaire with both closed and open-ended questions. Quantitative analysis was conducted and themes identified for qualitative analysis. Ethics approval was provided by the University of Pretoria and University of British Columbia. Results: There were 71 participants comprised of hospital managers, health and safety representatives, trade unions representatives and OSH professionals. At least 42% reported poor accessibility and poor timeliness of OHIS for decision-making. Only 50% had access to computers and 27% reported poor computer skills. When existing, OHIS was poorly organized and needed upgrades, with 85% reporting the need for significant reforms. Only 45% reported use of OHIS for decision-making in their OSH role. Conclusion: Given the gap in access and utilization of information needed to protect worker's rights to a safe and healthy workplace, more attention is warranted to OHIS development and use as well as education and training in South Africa and beyond.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.253
GPT teacher head0.527
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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